Abnormal Event Detection Method in Multimedia Sensor Networks

作     者：Li, Qi Liu, Xiaoming Yang, Xinyu Li, Ting 

作者机构：Beijing Univ Posts & Telecommun Beijing Peoples R China Coordinat Ctr China Natl Comp Network Emergency Response Tech Team Beijing 100876 Peoples R China 

出 版 物：《INTERNATIONAL JOURNAL OF DISTRIBUTED SENSOR NETWORKS》 (Int. J. Distrib. Sens. Netw.)

年 卷 期：2015年第11卷第11期

页      面：1-8页

核心收录：

学科分类：0810[工学-信息与通信工程] 08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：National Natural Science Foundation of China 

主　　题：MULTIMEDIA systems SENSOR networks CAMCORDERS COMPUTATIONAL complexity WEB-based instruction COMPUTER algorithms 

摘      要：Detecting abnormal events in multimedia sensor networks (MSNs) plays an increasingly essential role in our lives. Once video cameras cannot work (e.g., the sightline is blocked), audio sensor can provide us with critical information (e.g., in detecting the sound of gun-shot in the rainforest or the sound of car accident on a busy road). Audio sensors also have price advantage. Detecting abnormal audio events in complicated background environment is a very difficult problem;only few previous researches could offer good solution. In this paper, we proposed a novel method to detect the unexpected audio elements inmultimedia sensor networks. Firstly, we collect enough normal audio elements and then use statistical learning method to train them offline. On the basis of these models, we establish a background pool by prior knowledge. The background pool contains expected audio effects. Finally, we decide whether an audio event is unexpected by comparing it with the background pool. In this way, we reduce the complexity of online training while ensuring the detection accuracy. We designed some experiments to verify the effectiveness of the proposed method. In conclusion, the experiments show that the proposed algorithm can achieve satisfying results.